How to process big data is an ongoing challenge facing machine learning. Currently, the problem of machine learning processing large-scale data is very common. How to propose a machine learning algorithm that meets the needs of big data processing is a hot research topic in the era of big data. The "Big Data Machine Learning" course is a basic theoretical course for senior undergraduates and graduate students in the Department of Information Science. Its purpose is to train students to comprehensively understand the theoretical basis of big data machine learning and firmly master the methods and solutions of big data machine learning. Ability to solve practical problems. This course mainly studies machine learning and deep learning methods, aiming to realize the application of big data machine learning. The main contents of this course include:

1. Basic theory of statistical learning
2. .Basic methods of machine learning
3. Deep learning theories and methods

An ongoing challenge for machine learning is how to deal with big data. At present, the problem of machine learning dealing with large-scale data is widespread. How to propose a machine learning algorithm to meet the needs of big data processing is a hot research topic in the big data era. The course " Big Data Machine Learning" is a basic theory course for senior undergraduates and postgraduates in information science department. Its purpose is to cultivate students' comprehensive ability to understand the theoretical basis of Big Data Machine Learning, master the methods of Big Data Machine Learning firmly, and solve practical problems. This course focuses on the methodsof machine learning and deep learning, and aims to realize the application of big data machine learning. The main contents of the course include:

1. The basic theories of statistical learning
2. The basic methods of machine learning
3. The theories and methods of deep learning